Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site.

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We thank the reviewers for their comments and endorsements. Below are our answers to the main questions/concerns. R1: Training on test-fMRI samples - not convinced the approach is valid. We understand the reviewer's concern. Note however that our "training on test data" refers only to training on We will better clarify the distinction between training on the "test-fMRI" (which is the input to the network, We realize that this distinction is confusing, and will clarify it.

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.

In addition, we will add necessary details to the unclear parts in the article.

Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV -UNIL), Lausanne, Switzerland ABSTRACT Predicting cognition from neuroimaging data in healthy individuals offers insights into the neural mechanisms underlying cognitive abilities, with potential applications in precision medicine and early detection of neurological and psychiatric conditions. This study systematically benchmarked classical machine learning (Kernel Ridge Regression) and advanced deep learning models (Graph Neural Networks and Transformer-GNNs) for cognitive prediction using Resting-state, Working Memory, and Language task fMRI data from the Human Connectome Project Y oung Adult (HCP-Y A) dataset. Among the methods compared, a GNN combining structural and functional connectivity consistently achieved the highest performance across all fMRI modalities; however, its advantage over Kernel Ridge Regression using functional connectivity alone was not statistically significant. These findings emphasize the importance of selecting appropriate model architectures and feature representations to fully leverage the spatial and temporal richness of neuroimaging data. This study highlights the potential of multimodal graph-aware deep learning models to combine structural and functional connectivity for cognitive prediction, as well as the promise of Transformer-based approaches for capturing temporal dynamics. By providing a comprehensive comparison of models, this work serves as a guide for advancing brain-behavior modeling using fMRI, structural connectivity and deep learning. INTRODUCTION Understanding and predicting behavior from neuroimaging data in healthy individuals is crucial for advancing our knowledge of the brain's functional architecture and its relationship to behavior. While significant efforts have focused on patients with neurological or psychiatric disorders (Arbabshirani, Plis, Sui, & Calhoun, 2017; Sabuncu, Konukoglu, & Initiative, 2015), the study of healthy participants remains underexplored. Analyzing brain connectivity in healthy individuals can provide valuable insights into the baseline neural mechanisms underlying behavior, offering a foundation for early prognosis of potential neuro or psychiatric conditions (Bassett & Sporns, 2017; Fornito, Zalesky, & Breakspear, 2015; Lui, Zhou, Sweeney, & Gong, 2016; Zhou, Gennatas, Kramer, Miller, & Seeley, 2012). By examining the intricate patterns of functional and structural connectivity, we can identify biomarkers indicative of brain health, which can serve as early indicators of disease susceptibility (M.

Graph neural networks have been useful in machine learning on graph-structured data, particularly for node classification and some types of graph classification tasks. However, they have had limited use in representing patterning of signals over graphs. Patterning of signals over graphs and in subgraphs carries important information in many domains including neuroscience. Neural signals are spatiotemporally patterned, high dimensional and difficult to decode. Graph signal processing and associated GCN models utilize the graph Fourier transform and are unable to efficiently represent spatially or spectrally localized signal patterning on graphs. Wavelet transforms have shown promise here, but offer non-canonical representations and cannot be tightly confined to subgraphs. Here we propose SlepNet, a novel GCN architecture that uses Slepian bases rather than graph Fourier harmonics. In SlepNet, the Slepian harmonics optimally concentrate signal energy on specifically relevant subgraphs that are automatically learned with a mask. Thus, they can produce canonical and highly resolved representations of neural activity, focusing energy of harmonics on areas of the brain which are activated. We evaluated SlepNet across three fMRI datasets, spanning cognitive and visual tasks, and two traffic dynamics datasets, comparing its performance against conventional GNNs and graph signal processing constructs. SlepNet outperforms the baselines in all datasets. Moreover, the extracted representations of signal patterns from SlepNet offers more resolution in distinguishing between similar patterns, and thus represent brain signaling transients as informative trajectories. Here we have shown that these extracted trajectory representations can be used for other downstream untrained tasks. Thus we establish that SlepNet is useful both for prediction and representation learning in spatiotemporal data.

Recent advances in time series generation have shown promise, yet controlling properties in generated sequences remains challenging. Time Series Editing (TSE) - making precise modifications while preserving temporal coherence - consider both point-level constraints and segment-level controls that current methods struggle to provide. We introduce the CocktailEdit framework to enable simultaneous, flexible control across different types of constraints. This framework combines two key mechanisms: a confidence-weighted anchor control for point-wise constraints and a classifier-based control for managing statistical properties such as sums and averages over segments. Our methods achieve precise local control during the denoising inference stage while maintaining temporal coherence and integrating seamlessly, with any conditionally trained diffusion-based time series models. Extensive experiments across diverse datasets and models demonstrate its effectiveness. Our work bridges the gap between pure generative modeling and real-world time series editing needs, offering a flexible solution for human-in-the-loop time series generation and editing. The code and demo are provided for validation.